{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f75285b",
   "metadata": {},
   "outputs": [],
   "source": [
    "1. VAE (Variational Autoencoder) 结构:变分自编码器​\n",
    "AutoencoderKL(\n",
    "  (encoder): Encoder(\n",
    "    (conv_in): Conv2d(3, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "    (down_blocks): ModuleList(\n",
    "      (0): DownEncoderBlock2D(\n",
    "        (resnets): ModuleList(\n",
    "          (0-1): 2 x ResnetBlock2D(\n",
    "            (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
    "            (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "            (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
    "            (dropout): Dropout(p=0.0, inplace=False)\n",
    "            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "            (nonlinearity): SiLU()\n",
    "          )\n",
    "        )\n",
    "        (downsamplers): ModuleList(\n",
    "          (0): Downsample2D(\n",
    "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2))\n",
    "          )\n",
    "        )\n",
    "      )\n",
    "      (1): DownEncoderBlock2D(\n",
    "        (resnets): ModuleList(\n",
    "          (0): ResnetBlock2D(\n",
    "            (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
    "            (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "            (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
    "            (dropout): Dropout(p=0.0, inplace=False)\n",
    "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "            (nonlinearity): SiLU()\n",
    "            (conv_shortcut): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
    "          )\n",
    "          (1): ResnetBlock2D(\n",
    "            (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
    "            (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "            (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
    "            (dropout): Dropout(p=0.0, inplace=False)\n",
    "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "            (nonlinearity): SiLU()\n",
    "          )\n",
    "        )\n",
    "        (downsamplers): ModuleList(\n",
    "          (0): Downsample2D(\n",
    "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2))  #因为是代码做了padding，所以这里没有padding\n",
    "          )\n",
    "        )\n",
    "      )\n",
    "      (2): DownEncoderBlock2D(\n",
    "        (resnets): ModuleList(\n",
    "          (0): ResnetBlock2D(\n",
    "            (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
    "            (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "            (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
    "            (dropout): Dropout(p=0.0, inplace=False)\n",
    "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "            (nonlinearity): SiLU()\n",
    "            (conv_shortcut): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1))\n",
    "          )\n",
    "          (1): ResnetBlock2D(\n",
    "            (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
    "            (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "            (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
    "            (dropout): Dropout(p=0.0, inplace=False)\n",
    "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "            (nonlinearity): SiLU()\n",
    "          )\n",
    "        )\n",
    "        (downsamplers): ModuleList(\n",
    "          (0): Downsample2D(\n",
    "            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2))\n",
    "          )\n",
    "        )\n",
    "      )\n",
    "      (3): DownEncoderBlock2D(\n",
    "        (resnets): ModuleList(\n",
    "          (0-1): 2 x ResnetBlock2D(\n",
    "            (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
    "            (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "            (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
    "            (dropout): Dropout(p=0.0, inplace=False)\n",
    "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "            (nonlinearity): SiLU()\n",
    "          )\n",
    "        )\n",
    "      )\n",
    "    )\n",
    "    (mid_block): UNetMidBlock2D(\n",
    "      (attentions): ModuleList(\n",
    "        (0): Attention(\n",
    "          (group_norm): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
    "          (to_q): Linear(in_features=512, out_features=512, bias=True)\n",
    "          (to_k): Linear(in_features=512, out_features=512, bias=True)\n",
    "          (to_v): Linear(in_features=512, out_features=512, bias=True)\n",
    "          (to_out): ModuleList(\n",
    "            (0): Linear(in_features=512, out_features=512, bias=True)\n",
    "            (1): Dropout(p=0.0, inplace=False)\n",
    "          )\n",
    "        )\n",
    "      )\n",
    "      (resnets): ModuleList(\n",
    "        (0-1): 2 x ResnetBlock2D(\n",
    "          (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
    "          (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
    "          (dropout): Dropout(p=0.0, inplace=False)\n",
    "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (nonlinearity): SiLU()\n",
    "        )\n",
    "      )\n",
    "    )\n",
    "    (conv_norm_out): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
    "    (conv_act): SiLU()\n",
    "    (conv_out): Conv2d(512, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "  )\n",
    "  (decoder): Decoder(\n",
    "    (conv_in): Conv2d(4, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "    (up_blocks): ModuleList(\n",
    "      (0-1): 2 x UpDecoderBlock2D(\n",
    "        (resnets): ModuleList(\n",
    "          (0-2): 3 x ResnetBlock2D(\n",
    "            (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
    "            (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "            (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
    "            (dropout): Dropout(p=0.0, inplace=False)\n",
    "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "            (nonlinearity): SiLU()\n",
    "          )\n",
    "        )\n",
    "        (upsamplers): ModuleList(\n",
    "          (0): Upsample2D( #做了插值，图像尺寸变大,变大了2次\n",
    "            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          )\n",
    "        )\n",
    "      )\n",
    "      (2): UpDecoderBlock2D(\n",
    "        (resnets): ModuleList(\n",
    "          (0): ResnetBlock2D(\n",
    "            (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
    "            (conv1): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "            (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
    "            (dropout): Dropout(p=0.0, inplace=False)\n",
    "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "            (nonlinearity): SiLU()\n",
    "            (conv_shortcut): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
    "          )\n",
    "          (1-2): 2 x ResnetBlock2D(\n",
    "            (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
    "            (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "            (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
    "            (dropout): Dropout(p=0.0, inplace=False)\n",
    "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "            (nonlinearity): SiLU()\n",
    "          )\n",
    "        )\n",
    "        (upsamplers): ModuleList(\n",
    "          (0): Upsample2D( #做了插值，图像尺寸变大\n",
    "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          )\n",
    "        )\n",
    "      )\n",
    "      (3): UpDecoderBlock2D(\n",
    "        (resnets): ModuleList(\n",
    "          (0): ResnetBlock2D(\n",
    "            (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
    "            (conv1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "            (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
    "            (dropout): Dropout(p=0.0, inplace=False)\n",
    "            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "            (nonlinearity): SiLU()\n",
    "            (conv_shortcut): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))\n",
    "          )\n",
    "          (1-2): 2 x ResnetBlock2D(\n",
    "            (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
    "            (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "            (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
    "            (dropout): Dropout(p=0.0, inplace=False)\n",
    "            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "            (nonlinearity): SiLU()\n",
    "          )\n",
    "        )\n",
    "      )\n",
    "    )\n",
    "    (mid_block): UNetMidBlock2D( # 中间块，用于处理图像特征，夹在两个ResnetBlock中间\n",
    "      (attentions): ModuleList(\n",
    "        (0): Attention(\n",
    "          (group_norm): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
    "          (to_q): Linear(in_features=512, out_features=512, bias=True)\n",
    "          (to_k): Linear(in_features=512, out_features=512, bias=True)\n",
    "          (to_v): Linear(in_features=512, out_features=512, bias=True)\n",
    "          (to_out): ModuleList(\n",
    "            (0): Linear(in_features=512, out_features=512, bias=True)\n",
    "            (1): Dropout(p=0.0, inplace=False)\n",
    "          )\n",
    "        )\n",
    "      )\n",
    "      (resnets): ModuleList(\n",
    "        (0-1): 2 x ResnetBlock2D(\n",
    "          (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
    "          (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
    "          (dropout): Dropout(p=0.0, inplace=False)\n",
    "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (nonlinearity): SiLU()\n",
    "        )\n",
    "      )\n",
    "    )\n",
    "    (conv_norm_out): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
    "    (conv_act): SiLU()\n",
    "    (conv_out): Conv2d(128, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) # 输出3通道图像\n",
    "  )\n",
    "  (quant_conv): Conv2d(8, 8, kernel_size=(1, 1), stride=(1, 1)) \n",
    "  (post_quant_conv): Conv2d(4, 4, kernel_size=(1, 1), stride=(1, 1)) \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d317e0d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "2. Text Encoder 结构:\n",
    "CLIPTextModel(\n",
    "  (text_model): CLIPTextTransformer(\n",
    "    (embeddings): CLIPTextEmbeddings(\n",
    "      (token_embedding): Embedding(49408, 1024)\n",
    "      (position_embedding): Embedding(77, 1024)\n",
    "    )\n",
    "    (encoder): CLIPEncoder(\n",
    "      (layers): ModuleList(\n",
    "        (0-22): 23 x CLIPEncoderLayer(\n",
    "          (self_attn): CLIPAttention(\n",
    "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
    "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
    "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
    "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
    "          )\n",
    "          (layer_norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
    "          (mlp): CLIPMLP(\n",
    "            (activation_fn): GELUActivation()\n",
    "            (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
    "            (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
    "          )\n",
    "          (layer_norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
    "        )\n",
    "      )\n",
    "    )\n",
    "    (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
    "  )\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e298c93b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "3. Tokenizer 信息:\n",
    "CLIPTokenizer(name_or_path='/root/.cache/huggingface/hub/models--stabilityai--stable-diffusion-2-1-base/snapshots/5ede9e4bf3e3fd1cb0ef2f7a3fff13ee514fdf06/tokenizer', vocab_size=49408, model_max_length=77, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'bos_token': '<|startoftext|>', 'eos_token': '<|endoftext|>', 'unk_token': '<|endoftext|>', 'pad_token': '!'}, clean_up_tokenization_spaces=False, added_tokens_decoder={\n",
    "\t0: AddedToken(\"!\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
    "\t49406: AddedToken(\"<|startoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=True, special=True),\n",
    "\t49407: AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=True, special=True),\n",
    "}\n",
    ")\n",
    "\n",
    "4. U-Net 结构:\n",
    "UNet2DConditionModel(\n",
    "  (conv_in): Conv2d(4, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "  (time_proj): Timesteps()\n",
    "  (time_embedding): TimestepEmbedding(\n",
    "    (linear_1): Linear(in_features=320, out_features=1280, bias=True)\n",
    "    (act): SiLU()\n",
    "    (linear_2): Linear(in_features=1280, out_features=1280, bias=True)\n",
    "  )\n",
    "  (down_blocks): ModuleList(\n",
    "    (0): CrossAttnDownBlock2D( #要到源码中查看如何处理seq len\n",
    "      (attentions): ModuleList(\n",
    "        (0-1): 2 x Transformer2DModel(\n",
    "          (norm): GroupNorm(32, 320, eps=1e-06, affine=True)\n",
    "          (proj_in): Linear(in_features=320, out_features=320, bias=True)\n",
    "          (transformer_blocks): ModuleList(\n",
    "            (0): BasicTransformerBlock(\n",
    "              (norm1): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\n",
    "              (attn1): Attention(\n",
    "                (to_q): Linear(in_features=320, out_features=320, bias=False)\n",
    "                (to_k): Linear(in_features=320, out_features=320, bias=False)\n",
    "                (to_v): Linear(in_features=320, out_features=320, bias=False)\n",
    "                (to_out): ModuleList(\n",
    "                  (0): Linear(in_features=320, out_features=320, bias=True)\n",
    "                  (1): Dropout(p=0.0, inplace=False)\n",
    "                )\n",
    "              )\n",
    "              (norm2): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\n",
    "              (attn2): Attention(\n",
    "                (to_q): Linear(in_features=320, out_features=320, bias=False)\n",
    "                (to_k): Linear(in_features=1024, out_features=320, bias=False) #1024来源于上面的CLIPText\n",
    "                (to_v): Linear(in_features=1024, out_features=320, bias=False)\n",
    "                (to_out): ModuleList(\n",
    "                  (0): Linear(in_features=320, out_features=320, bias=True)\n",
    "                  (1): Dropout(p=0.0, inplace=False)\n",
    "                )\n",
    "              )\n",
    "              (norm3): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\n",
    "              (ff): FeedForward(\n",
    "                (net): ModuleList(\n",
    "                  (0): GEGLU(\n",
    "                    (proj): Linear(in_features=320, out_features=2560, bias=True)\n",
    "                  )\n",
    "                  (1): Dropout(p=0.0, inplace=False)\n",
    "                  (2): Linear(in_features=1280, out_features=320, bias=True)\n",
    "                )\n",
    "              )\n",
    "            )\n",
    "          )\n",
    "          (proj_out): Linear(in_features=320, out_features=320, bias=True)\n",
    "        )\n",
    "      )\n",
    "      (resnets): ModuleList(\n",
    "        (0-1): 2 x ResnetBlock2D(\n",
    "          (norm1): GroupNorm(32, 320, eps=1e-05, affine=True)\n",
    "          (conv1): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (time_emb_proj): Linear(in_features=1280, out_features=320, bias=True)\n",
    "          (norm2): GroupNorm(32, 320, eps=1e-05, affine=True)\n",
    "          (dropout): Dropout(p=0.0, inplace=False)\n",
    "          (conv2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (nonlinearity): SiLU()\n",
    "        )\n",
    "      )\n",
    "      (downsamplers): ModuleList( #图像尺寸减半\n",
    "        (0): Downsample2D(\n",
    "          (conv): Conv2d(320, 320, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
    "        )\n",
    "      )\n",
    "    )\n",
    "    (1): CrossAttnDownBlock2D(\n",
    "      (attentions): ModuleList(\n",
    "        (0-1): 2 x Transformer2DModel(\n",
    "          (norm): GroupNorm(32, 640, eps=1e-06, affine=True)\n",
    "          (proj_in): Linear(in_features=640, out_features=640, bias=True)\n",
    "          (transformer_blocks): ModuleList(\n",
    "            (0): BasicTransformerBlock(\n",
    "              (norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\n",
    "              (attn1): Attention(\n",
    "                (to_q): Linear(in_features=640, out_features=640, bias=False)\n",
    "                (to_k): Linear(in_features=640, out_features=640, bias=False)\n",
    "                (to_v): Linear(in_features=640, out_features=640, bias=False)\n",
    "                (to_out): ModuleList(\n",
    "                  (0): Linear(in_features=640, out_features=640, bias=True)\n",
    "                  (1): Dropout(p=0.0, inplace=False)\n",
    "                )\n",
    "              )\n",
    "              (norm2): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\n",
    "              (attn2): Attention(\n",
    "                (to_q): Linear(in_features=640, out_features=640, bias=False)\n",
    "                (to_k): Linear(in_features=1024, out_features=640, bias=False)\n",
    "                (to_v): Linear(in_features=1024, out_features=640, bias=False)\n",
    "                (to_out): ModuleList(\n",
    "                  (0): Linear(in_features=640, out_features=640, bias=True)\n",
    "                  (1): Dropout(p=0.0, inplace=False)\n",
    "                )\n",
    "              )\n",
    "              (norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\n",
    "              (ff): FeedForward(\n",
    "                (net): ModuleList(\n",
    "                  (0): GEGLU(\n",
    "                    (proj): Linear(in_features=640, out_features=5120, bias=True)\n",
    "                  )\n",
    "                  (1): Dropout(p=0.0, inplace=False)\n",
    "                  (2): Linear(in_features=2560, out_features=640, bias=True)\n",
    "                )\n",
    "              )\n",
    "            )\n",
    "          )\n",
    "          (proj_out): Linear(in_features=640, out_features=640, bias=True)\n",
    "        )\n",
    "      )\n",
    "      (resnets): ModuleList(\n",
    "        (0): ResnetBlock2D(\n",
    "          (norm1): GroupNorm(32, 320, eps=1e-05, affine=True)\n",
    "          (conv1): Conv2d(320, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)\n",
    "          (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
    "          (dropout): Dropout(p=0.0, inplace=False)\n",
    "          (conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (nonlinearity): SiLU()\n",
    "          (conv_shortcut): Conv2d(320, 640, kernel_size=(1, 1), stride=(1, 1))\n",
    "        )\n",
    "        (1): ResnetBlock2D(\n",
    "          (norm1): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
    "          (conv1): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)\n",
    "          (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
    "          (dropout): Dropout(p=0.0, inplace=False)\n",
    "          (conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (nonlinearity): SiLU()\n",
    "        )\n",
    "      )\n",
    "      (downsamplers): ModuleList( #减半\n",
    "        (0): Downsample2D(\n",
    "          (conv): Conv2d(640, 640, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
    "        )\n",
    "      )\n",
    "    )\n",
    "    (2): CrossAttnDownBlock2D(\n",
    "      (attentions): ModuleList(\n",
    "        (0-1): 2 x Transformer2DModel(\n",
    "          (norm): GroupNorm(32, 1280, eps=1e-06, affine=True)\n",
    "          (proj_in): Linear(in_features=1280, out_features=1280, bias=True)\n",
    "          (transformer_blocks): ModuleList(\n",
    "            (0): BasicTransformerBlock(\n",
    "              (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
    "              (attn1): Attention(\n",
    "                (to_q): Linear(in_features=1280, out_features=1280, bias=False)\n",
    "                (to_k): Linear(in_features=1280, out_features=1280, bias=False)\n",
    "                (to_v): Linear(in_features=1280, out_features=1280, bias=False)\n",
    "                (to_out): ModuleList(\n",
    "                  (0): Linear(in_features=1280, out_features=1280, bias=True)\n",
    "                  (1): Dropout(p=0.0, inplace=False)\n",
    "                )\n",
    "              )\n",
    "              (norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
    "              (attn2): Attention(\n",
    "                (to_q): Linear(in_features=1280, out_features=1280, bias=False)\n",
    "                (to_k): Linear(in_features=1024, out_features=1280, bias=False)\n",
    "                (to_v): Linear(in_features=1024, out_features=1280, bias=False)\n",
    "                (to_out): ModuleList(\n",
    "                  (0): Linear(in_features=1280, out_features=1280, bias=True)\n",
    "                  (1): Dropout(p=0.0, inplace=False)\n",
    "                )\n",
    "              )\n",
    "              (norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
    "              (ff): FeedForward(\n",
    "                (net): ModuleList(\n",
    "                  (0): GEGLU(\n",
    "                    (proj): Linear(in_features=1280, out_features=10240, bias=True)\n",
    "                  )\n",
    "                  (1): Dropout(p=0.0, inplace=False)\n",
    "                  (2): Linear(in_features=5120, out_features=1280, bias=True)\n",
    "                )\n",
    "              )\n",
    "            )\n",
    "          )\n",
    "          (proj_out): Linear(in_features=1280, out_features=1280, bias=True)\n",
    "        )\n",
    "      )\n",
    "      (resnets): ModuleList(\n",
    "        (0): ResnetBlock2D(\n",
    "          (norm1): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
    "          (conv1): Conv2d(640, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
    "          (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
    "          (dropout): Dropout(p=0.0, inplace=False)\n",
    "          (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (nonlinearity): SiLU()\n",
    "          (conv_shortcut): Conv2d(640, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
    "        )\n",
    "        (1): ResnetBlock2D(\n",
    "          (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
    "          (conv1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
    "          (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
    "          (dropout): Dropout(p=0.0, inplace=False)\n",
    "          (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (nonlinearity): SiLU()\n",
    "        )\n",
    "      )\n",
    "      (downsamplers): ModuleList( #降采样，变一半\n",
    "        (0): Downsample2D(\n",
    "          (conv): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
    "        )\n",
    "      )\n",
    "    )\n",
    "    (3): DownBlock2D(\n",
    "      (resnets): ModuleList(\n",
    "        (0-1): 2 x ResnetBlock2D(\n",
    "          (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
    "          (conv1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
    "          (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
    "          (dropout): Dropout(p=0.0, inplace=False)\n",
    "          (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (nonlinearity): SiLU()\n",
    "        )\n",
    "      )\n",
    "    )\n",
    "  )\n",
    "  (up_blocks): ModuleList(\n",
    "    (0): UpBlock2D(\n",
    "      (resnets): ModuleList( #down的输出是2560,和up的输入是1280,所以这里需要一个ResnetBlock2D来处理\n",
    "        (0-2): 3 x ResnetBlock2D(\n",
    "          (norm1): GroupNorm(32, 2560, eps=1e-05, affine=True)\n",
    "          (conv1): Conv2d(2560, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
    "          (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
    "          (dropout): Dropout(p=0.0, inplace=False)\n",
    "          (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (nonlinearity): SiLU()\n",
    "          (conv_shortcut): Conv2d(2560, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
    "        )\n",
    "      )\n",
    "      (upsamplers): ModuleList(\n",
    "        (0): Upsample2D(\n",
    "          (conv): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "        )\n",
    "      )\n",
    "    )\n",
    "    (1): CrossAttnUpBlock2D(\n",
    "      (attentions): ModuleList(\n",
    "        (0-2): 3 x Transformer2DModel(\n",
    "          (norm): GroupNorm(32, 1280, eps=1e-06, affine=True)\n",
    "          (proj_in): Linear(in_features=1280, out_features=1280, bias=True)\n",
    "          (transformer_blocks): ModuleList(\n",
    "            (0): BasicTransformerBlock(\n",
    "              (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
    "              (attn1): Attention(\n",
    "                (to_q): Linear(in_features=1280, out_features=1280, bias=False)\n",
    "                (to_k): Linear(in_features=1280, out_features=1280, bias=False)\n",
    "                (to_v): Linear(in_features=1280, out_features=1280, bias=False)\n",
    "                (to_out): ModuleList(\n",
    "                  (0): Linear(in_features=1280, out_features=1280, bias=True)\n",
    "                  (1): Dropout(p=0.0, inplace=False)\n",
    "                )\n",
    "              )\n",
    "              (norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
    "              (attn2): Attention(\n",
    "                (to_q): Linear(in_features=1280, out_features=1280, bias=False)\n",
    "                (to_k): Linear(in_features=1024, out_features=1280, bias=False)\n",
    "                (to_v): Linear(in_features=1024, out_features=1280, bias=False)\n",
    "                (to_out): ModuleList(\n",
    "                  (0): Linear(in_features=1280, out_features=1280, bias=True)\n",
    "                  (1): Dropout(p=0.0, inplace=False)\n",
    "                )\n",
    "              )\n",
    "              (norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
    "              (ff): FeedForward(\n",
    "                (net): ModuleList(\n",
    "                  (0): GEGLU(\n",
    "                    (proj): Linear(in_features=1280, out_features=10240, bias=True)\n",
    "                  )\n",
    "                  (1): Dropout(p=0.0, inplace=False)\n",
    "                  (2): Linear(in_features=5120, out_features=1280, bias=True)\n",
    "                )\n",
    "              )\n",
    "            )\n",
    "          )\n",
    "          (proj_out): Linear(in_features=1280, out_features=1280, bias=True)\n",
    "        )\n",
    "      )\n",
    "      (resnets): ModuleList(\n",
    "        (0-1): 2 x ResnetBlock2D(\n",
    "          (norm1): GroupNorm(32, 2560, eps=1e-05, affine=True)\n",
    "          (conv1): Conv2d(2560, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
    "          (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
    "          (dropout): Dropout(p=0.0, inplace=False)\n",
    "          (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (nonlinearity): SiLU()\n",
    "          (conv_shortcut): Conv2d(2560, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
    "        )\n",
    "        (2): ResnetBlock2D(\n",
    "          (norm1): GroupNorm(32, 1920, eps=1e-05, affine=True)\n",
    "          (conv1): Conv2d(1920, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
    "          (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
    "          (dropout): Dropout(p=0.0, inplace=False)\n",
    "          (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (nonlinearity): SiLU()\n",
    "          (conv_shortcut): Conv2d(1920, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
    "        )\n",
    "      )\n",
    "      (upsamplers): ModuleList(\n",
    "        (0): Upsample2D(\n",
    "          (conv): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "        )\n",
    "      )\n",
    "    )\n",
    "    (2): CrossAttnUpBlock2D(\n",
    "      (attentions): ModuleList(\n",
    "        (0-2): 3 x Transformer2DModel(\n",
    "          (norm): GroupNorm(32, 640, eps=1e-06, affine=True)\n",
    "          (proj_in): Linear(in_features=640, out_features=640, bias=True)\n",
    "          (transformer_blocks): ModuleList(\n",
    "            (0): BasicTransformerBlock(\n",
    "              (norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\n",
    "              (attn1): Attention(\n",
    "                (to_q): Linear(in_features=640, out_features=640, bias=False)\n",
    "                (to_k): Linear(in_features=640, out_features=640, bias=False)\n",
    "                (to_v): Linear(in_features=640, out_features=640, bias=False)\n",
    "                (to_out): ModuleList(\n",
    "                  (0): Linear(in_features=640, out_features=640, bias=True)\n",
    "                  (1): Dropout(p=0.0, inplace=False)\n",
    "                )\n",
    "              )\n",
    "              (norm2): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\n",
    "              (attn2): Attention(\n",
    "                (to_q): Linear(in_features=640, out_features=640, bias=False)\n",
    "                (to_k): Linear(in_features=1024, out_features=640, bias=False)\n",
    "                (to_v): Linear(in_features=1024, out_features=640, bias=False)\n",
    "                (to_out): ModuleList(\n",
    "                  (0): Linear(in_features=640, out_features=640, bias=True)\n",
    "                  (1): Dropout(p=0.0, inplace=False)\n",
    "                )\n",
    "              )\n",
    "              (norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\n",
    "              (ff): FeedForward(\n",
    "                (net): ModuleList(\n",
    "                  (0): GEGLU(\n",
    "                    (proj): Linear(in_features=640, out_features=5120, bias=True)\n",
    "                  )\n",
    "                  (1): Dropout(p=0.0, inplace=False)\n",
    "                  (2): Linear(in_features=2560, out_features=640, bias=True)\n",
    "                )\n",
    "              )\n",
    "            )\n",
    "          )\n",
    "          (proj_out): Linear(in_features=640, out_features=640, bias=True)\n",
    "        )\n",
    "      )\n",
    "      (resnets): ModuleList(\n",
    "        (0): ResnetBlock2D(\n",
    "          (norm1): GroupNorm(32, 1920, eps=1e-05, affine=True)\n",
    "          (conv1): Conv2d(1920, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)\n",
    "          (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
    "          (dropout): Dropout(p=0.0, inplace=False)\n",
    "          (conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (nonlinearity): SiLU()\n",
    "          (conv_shortcut): Conv2d(1920, 640, kernel_size=(1, 1), stride=(1, 1))\n",
    "        )\n",
    "        (1): ResnetBlock2D(\n",
    "          (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
    "          (conv1): Conv2d(1280, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)\n",
    "          (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
    "          (dropout): Dropout(p=0.0, inplace=False)\n",
    "          (conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (nonlinearity): SiLU()\n",
    "          (conv_shortcut): Conv2d(1280, 640, kernel_size=(1, 1), stride=(1, 1))\n",
    "        )\n",
    "        (2): ResnetBlock2D(\n",
    "          (norm1): GroupNorm(32, 960, eps=1e-05, affine=True)\n",
    "          (conv1): Conv2d(960, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)\n",
    "          (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
    "          (dropout): Dropout(p=0.0, inplace=False)\n",
    "          (conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (nonlinearity): SiLU()\n",
    "          (conv_shortcut): Conv2d(960, 640, kernel_size=(1, 1), stride=(1, 1))\n",
    "        )\n",
    "      )\n",
    "      (upsamplers): ModuleList(\n",
    "        (0): Upsample2D(\n",
    "          (conv): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "        )\n",
    "      )\n",
    "    )\n",
    "    (3): CrossAttnUpBlock2D(\n",
    "      (attentions): ModuleList(\n",
    "        (0-2): 3 x Transformer2DModel(\n",
    "          (norm): GroupNorm(32, 320, eps=1e-06, affine=True)\n",
    "          (proj_in): Linear(in_features=320, out_features=320, bias=True)\n",
    "          (transformer_blocks): ModuleList(\n",
    "            (0): BasicTransformerBlock(\n",
    "              (norm1): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\n",
    "              (attn1): Attention(\n",
    "                (to_q): Linear(in_features=320, out_features=320, bias=False)\n",
    "                (to_k): Linear(in_features=320, out_features=320, bias=False)\n",
    "                (to_v): Linear(in_features=320, out_features=320, bias=False)\n",
    "                (to_out): ModuleList(\n",
    "                  (0): Linear(in_features=320, out_features=320, bias=True)\n",
    "                  (1): Dropout(p=0.0, inplace=False)\n",
    "                )\n",
    "              )\n",
    "              (norm2): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\n",
    "              (attn2): Attention(\n",
    "                (to_q): Linear(in_features=320, out_features=320, bias=False)\n",
    "                (to_k): Linear(in_features=1024, out_features=320, bias=False)\n",
    "                (to_v): Linear(in_features=1024, out_features=320, bias=False)\n",
    "                (to_out): ModuleList(\n",
    "                  (0): Linear(in_features=320, out_features=320, bias=True)\n",
    "                  (1): Dropout(p=0.0, inplace=False)\n",
    "                )\n",
    "              )\n",
    "              (norm3): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\n",
    "              (ff): FeedForward(\n",
    "                (net): ModuleList(\n",
    "                  (0): GEGLU(\n",
    "                    (proj): Linear(in_features=320, out_features=2560, bias=True)\n",
    "                  )\n",
    "                  (1): Dropout(p=0.0, inplace=False)\n",
    "                  (2): Linear(in_features=1280, out_features=320, bias=True)\n",
    "                )\n",
    "              )\n",
    "            )\n",
    "          )\n",
    "          (proj_out): Linear(in_features=320, out_features=320, bias=True)\n",
    "        )\n",
    "      )\n",
    "      (resnets): ModuleList(\n",
    "        (0): ResnetBlock2D(\n",
    "          (norm1): GroupNorm(32, 960, eps=1e-05, affine=True)\n",
    "          (conv1): Conv2d(960, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (time_emb_proj): Linear(in_features=1280, out_features=320, bias=True)\n",
    "          (norm2): GroupNorm(32, 320, eps=1e-05, affine=True)\n",
    "          (dropout): Dropout(p=0.0, inplace=False)\n",
    "          (conv2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (nonlinearity): SiLU()\n",
    "          (conv_shortcut): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1))\n",
    "        )\n",
    "        (1-2): 2 x ResnetBlock2D(\n",
    "          (norm1): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
    "          (conv1): Conv2d(640, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (time_emb_proj): Linear(in_features=1280, out_features=320, bias=True)\n",
    "          (norm2): GroupNorm(32, 320, eps=1e-05, affine=True)\n",
    "          (dropout): Dropout(p=0.0, inplace=False)\n",
    "          (conv2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "          (nonlinearity): SiLU()\n",
    "          (conv_shortcut): Conv2d(640, 320, kernel_size=(1, 1), stride=(1, 1))\n",
    "        )\n",
    "      )\n",
    "    )\n",
    "  )\n",
    "  mid的作用在课件有讲\n",
    "  (mid_block): UNetMidBlock2DCrossAttn(\n",
    "    (attentions): ModuleList(\n",
    "      (0): Transformer2DModel(\n",
    "        (norm): GroupNorm(32, 1280, eps=1e-06, affine=True)\n",
    "        (proj_in): Linear(in_features=1280, out_features=1280, bias=True)\n",
    "        (transformer_blocks): ModuleList(\n",
    "          (0): BasicTransformerBlock(\n",
    "            (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
    "            (attn1): Attention(\n",
    "              (to_q): Linear(in_features=1280, out_features=1280, bias=False)\n",
    "              (to_k): Linear(in_features=1280, out_features=1280, bias=False)\n",
    "              (to_v): Linear(in_features=1280, out_features=1280, bias=False)\n",
    "              (to_out): ModuleList(\n",
    "                (0): Linear(in_features=1280, out_features=1280, bias=True)\n",
    "                (1): Dropout(p=0.0, inplace=False)\n",
    "              )\n",
    "            )\n",
    "            (norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
    "            (attn2): Attention(\n",
    "              (to_q): Linear(in_features=1280, out_features=1280, bias=False)\n",
    "              (to_k): Linear(in_features=1024, out_features=1280, bias=False)\n",
    "              (to_v): Linear(in_features=1024, out_features=1280, bias=False)\n",
    "              (to_out): ModuleList(\n",
    "                (0): Linear(in_features=1280, out_features=1280, bias=True)\n",
    "                (1): Dropout(p=0.0, inplace=False)\n",
    "              )\n",
    "            )\n",
    "            (norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
    "            (ff): FeedForward(\n",
    "              (net): ModuleList(\n",
    "                (0): GEGLU(\n",
    "                  (proj): Linear(in_features=1280, out_features=10240, bias=True)\n",
    "                )\n",
    "                (1): Dropout(p=0.0, inplace=False)\n",
    "                (2): Linear(in_features=5120, out_features=1280, bias=True)\n",
    "              )\n",
    "            )\n",
    "          )\n",
    "        )\n",
    "        (proj_out): Linear(in_features=1280, out_features=1280, bias=True)\n",
    "      )\n",
    "    )\n",
    "    (resnets): ModuleList(\n",
    "      (0-1): 2 x ResnetBlock2D(\n",
    "        (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
    "        (conv1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "        (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
    "        (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
    "        (dropout): Dropout(p=0.0, inplace=False)\n",
    "        (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "        (nonlinearity): SiLU()\n",
    "      )\n",
    "    )\n",
    "  )\n",
    "  (conv_norm_out): GroupNorm(32, 320, eps=1e-05, affine=True)\n",
    "  (conv_act): SiLU()\n",
    "  (conv_out): Conv2d(320, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9e0e0bcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):\n",
    "    \"\"\"\n",
    "    Create sinusoidal timestep embeddings.\n",
    "    :param timesteps: a 1-D Tensor of N indices, one per batch element.\n",
    "                      These may be fractional.\n",
    "    :param dim: the dimension of the output.\n",
    "    :param max_period: controls the minimum frequency of the embeddings.\n",
    "    :return: an [N x dim] Tensor of positional embeddings.\n",
    "    \"\"\"\n",
    "    if not repeat_only:  # 如果不仅仅是重复时间步\n",
    "        half = dim // 2  # 计算维度的一半\n",
    "        freqs = torch.exp(  # 计算频率\n",
    "            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half  # 生成一个从0到half-1的等差数列并进行缩放\n",
    "        ).to(device=timesteps.device)  # 将频率张量移动到与timesteps相同的设备上\n",
    "        args = timesteps[:, None].float() * freqs[None]  # 将时间步扩展为列向量并乘以频率\n",
    "        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)  # 连接余弦和正弦部分形成嵌入\n",
    "        if dim % 2:  # 如果维度是奇数\n",
    "            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)  # 添加一个零列以匹配维度\n",
    "    else:  # 如果仅仅是重复时间步\n",
    "        # embedding = repeat(timesteps, 'b -> b d', d=dim)  # 将时间步重复dim次\n",
    "        pass\n",
    "    return embedding  # 返回嵌入结果\n",
    "\n",
    "# 示例：生成并可视化timestep embedding\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import math\n",
    "# from einops import repeat\n",
    "\n",
    "# 创建一系列时间步\n",
    "timesteps = torch.arange(0, 1000, 100)  # 0到900，步长为100\n",
    "dim = 128  # 嵌入维度\n",
    "\n",
    "# 生成嵌入\n",
    "embeddings = timestep_embedding(timesteps, dim)\n",
    "\n",
    "# 可视化嵌入\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# 热力图展示所有嵌入\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.imshow(embeddings.cpu().numpy(), aspect='auto', cmap='viridis')\n",
    "plt.colorbar()\n",
    "plt.title('Timestep Embeddings Heatmap')\n",
    "plt.xlabel('Embedding Dimension')\n",
    "plt.ylabel('Timestep Index')\n",
    "\n",
    "# 选择几个时间步的嵌入进行线图展示\n",
    "plt.subplot(1, 2, 2)\n",
    "selected_indices = [0, 3, 6, 9]  # 选择几个时间步\n",
    "for idx in selected_indices:\n",
    "    plt.plot(embeddings[idx].cpu().numpy(), label=f'Timestep {timesteps[idx].item()}')\n",
    "plt.legend()\n",
    "plt.title('Selected Timestep Embeddings')\n",
    "plt.xlabel('Embedding Dimension')\n",
    "plt.ylabel('Value')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ff5ab065",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2 [3, 4]\n"
     ]
    }
   ],
   "source": [
    "b, c, *spatial=1,2,3,4\n",
    "print(b, c, spatial)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  }
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